---

name: json-validation description: Pydantic schema validation for agent outputs. Use to validate JSON against TableOfContents, ClinicalSummary, TreatmentPlan, and EvaluatorFeedback schemas before accepting agent responses.

JSON Validation Skill

Overview

This skill provides utilities for validating JSON outputs against Pydantic schemas to ensure data integrity and schema compliance. It handles LLM output parsing, JSON extraction, and detailed error reporting.

When to Use

Use this skill to:

• Validate agent outputs before accepting them
• Parse JSON from LLM text responses (handles embedded JSON)
• Ensure schema compliance for TableOfContents, ClinicalSummary, TreatmentPlan
• Get detailed validation error messages
• Enforce data constraints (offsets, citations, confidence scores)

Installation

IMPORTANT: This skill has its own isolated virtual environment (.venv) managed by uv. Do NOT use system Python.

Initialize the skill's environment:

Copy# From the skill directory
cd .agent/skills/json-validation
uv sync  # Creates .venv and installs dependencies from pyproject.toml

Dependencies are in pyproject.toml:

• pydantic - Schema validation

Usage

CRITICAL: Always use uv run to execute code with this skill's .venv, NOT system Python.

Validate Python Dictionary

Copy# From .agent/skills/json-validation/ directory
# Run with: uv run python -c "..."
from json_validation import JSONValidator

# You'll need to import schemas separately
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).parent.parent.parent / "src/models"))
from toc_schema import TableOfContents

# Data to validate
toc_data = {
    "sections": [
        {
            "title": "History of Present Illness",
            "start_offset": 0,
            "end_offset": 450,
            "is_explicit": True,
            "confidence": 0.98
        }
    ],
    "total_sections": 1,
    "navigation_enabled": True
}

# Validate against schema
result = JSONValidator.validate(toc_data, TableOfContents)

if result['valid']:
    validated_toc = result['validated_data']
    print("Validation passed!")
    print(validated_toc)
else:
    print("Validation failed:")
    for error in result['errors']:
        print(f"  - {error}")

Validate JSON String

Copy# JSON as string
toc_json = '{"sections": [...], "total_sections": 1, "navigation_enabled": true}'

result = JSONValidator.validate_json_string(toc_json, TableOfContents)

if result['valid']:
    print("Valid JSON and schema")
else:
    print(f"Errors: {result['errors']}")

Parse LLM Output with Embedded JSON

Copy# LLM often returns JSON embedded in explanatory text
llm_output = """
Here's the clinical summary:

{
  "patient_snapshot": {"age": 45, "sex": "M", "chief_complaint": "Chest pain"},
  "key_problems": [...]
}

This summary covers the main clinical findings.
"""

# Extract and validate JSON automatically
from src.models.summary_schema import ClinicalSummary

result = JSONValidator.validate_llm_output(llm_output, ClinicalSummary)

if result['valid']:
    summary = result['validated_data']
    print(f"Extracted and validated summary: {summary['patient_snapshot']}")
else:
    print(f"Could not extract valid JSON: {result['errors']}")

Validation with All Schemas

Copyfrom src.models.toc_schema import TableOfContents
from src.models.summary_schema import ClinicalSummary
from src.models.plan_schema import TreatmentPlan
from src.models.evaluator_schema import EvaluatorFeedback

# Validate different agent outputs
schemas = {
    "toc": TableOfContents,
    "summary": ClinicalSummary,
    "plan": TreatmentPlan,
    "evaluator": EvaluatorFeedback
}

for output_type, schema in schemas.items():
    result = JSONValidator.validate_llm_output(agent_outputs[output_type], schema)
    
    if not result['valid']:
        print(f"{output_type} validation failed:")
        for error in result['errors']:
            print(f"  - {error}")

Common Validation Rules

TableOfContents:

• Sections must not overlap
• start_offset < end_offset
• Sections ordered by start_offset

ClinicalSummary:

• All citations must have jaccard_overlap ≥ 0.7
• key_problems cannot be empty
• Citation offsets must be valid

TreatmentPlan:

• At least 3 recommendations required
• Recommendations ordered by priority_level
• If confidence_score < 0.6, hallucination_guard_note required

EvaluatorFeedback:

• If status == "fail", issues and suggestions required
• iteration_number must be 1-5

Error Messages

Validation errors include field paths:

Copysections -> 0 -> end_offset: end_offset must be greater than start_offset
total_sections: total_sections must equal len(sections)
key_problems: ensure this value has at least 1 items

Best Practices

1. Always Validate: Never accept agent outputs without validation
2. Handle Errors Gracefully: Retry with feedback if validation fails
3. Log Validation Failures: Use structured-logging skill to track issues
4. Extract from LLM: Use validate_llm_output() for robustness against LLM formatting
5. Provide Feedback: Pass validation errors to Evaluator Agent for refinement

Integration with Iterative Refinement

Copyfrom src.skills.json_validation.json_validation import JSONValidator
from src.skills.ollama_client.ollama_client import OllamaClient
from src.models.summary_schema import ClinicalSummary

client = OllamaClient()
max_retries = 5

for attempt in range(1, max_retries + 1):
    # Get LLM output
    llm_response = client.generate(prompt=summary_prompt)
    
    # Validate
    result = JSONValidator.validate_llm_output(
        llm_response["response"],
        ClinicalSummary
    )
    
    if result['valid']:
        print(f"Valid summary on attempt {attempt}")
        summary = result['validated_data']
        break
    else:
        print(f"Attempt {attempt} failed validation:")
        for error in result['errors']:
            print(f"  - {error}")
        
        # Add errors to next prompt for refinement
        summary_prompt += f"\n\nPrevious attempt had errors: {result['errors']}"
else:
    print("Failed to get valid summary after max retries")

Debugging Tips

1. Check Field Paths: Error messages include exact field location
2. Review Schema: Look at Pydantic models in src/models/ for constraints
3. Test with Minimal Data: Start with simple valid examples
4. Incremental Complexity: Add fields one at a time to isolate issues

Implementation

See json_validation.py for the full Python implementation.